FIG. 1 is a schematic diagram of a prior art multi-sensor antenna receiver. The antenna includes N sensors 100i (i=1 . . . N) for detecting perturbed signals xi(t) where i=1 . . . N and t is the time. These signals are filtered by channel filters 110i, where i=1 . . . N, for monitoring several features of the antenna system, namely
to assure that the antenna exhibits a particular directivity (aperture of the main lobe, magnitude of the secondary lobes, rejection in undesired directions, etc).
to allow pointing the antenna in the direction of the desired signal
The last stage derives the output of the antenna signal y(t) by summing the signals from the channel filters using a summer 150. The design of these filters in particular depends on the geometry of the detector array and on the kind of signals to be processed.
In a given application, the antenna performance may be mediocre. In particular, reduction of interferences may be inadequate. Said reduction is a feature of antenna effectiveness and is denoted by the antenna's SN (signal to noise) gain. The term “noise” herein denotes the set of interfering signals that the antenna must reduce. In order to increase the signal to noise ratio, it is known to post-filter the antenna output signal. FIG. 2 shows the principle of operation of such a filter.
The combination of a multisensor antenna and post filtering was first described by J B Allen in 1977 [Allen 77]. This technique was described to remove reverberation from a voice signal when detecting remote sound in an interfering medium. The sound is picked up by two microphones and the full processing (estimating the post-filter, applying and post-filtering)—which is based on the coherence function—is carried out in the frequency domain. In 1988 R Zelinski [Zelinkski 88] extended these techniques to recording sound using more sensors. K U Simmer [Simmer 92a] proposed stating the post-filter transfer function according to a Wiener filtering method. The analysis below describes the above methods. Further details are found in [Marro 98].
Assuming that the post-filtering illustrated in FIG. 2 relates to sound detection, the noise signals xi (n) are picked up by an antenna composed of N microphones (2001), I=1 . . . N, wherexi(n)=s(n−τM)+ni(n),  (1)where i=1 . . . N, s is the desired voice signal and nl is the noise level at the pickup 200l. Because of the “digital signal processing” formalism, n in this instance stands for the discrete time coefficient, ΕMi is the delay inserted by propagation between the sound from the source s(n) and that reaching the microphone 200i. To shift this signal in phase again (i.e., pointing the antenna in the direction of the source), the antenna is aimed in the direction of the desired emitter by means of filters r:(n)vi(n)=ri(n)*xi(n),  (2)where i=1 . . . N, and vi(n) is xl(n) delayed. As shown in FIG. 2, each microphone signal xi (n) is subjected to a delay τl (this delay being provided by the filter ri(n)). The signals Vi(f) represent signals vi(n) in the frequency domain, f denoting frequency. This operation is carried out using DFT (discrete Fourier transform) blocks. The 1/N multiplier which is applied following channel summation is a normalization coefficient assuring that the antenna gain is unity for the desired signal. The gain is an integral part of the antenna and provides the output signal Y(f). The post filter 260, having a transfer function W(f) which is estimated from the channel signals Vi)f) and/or from the antenna output Y(f) (the way of calculating W(f) is described below), is applied to Y(f). The last synthesizing block converts the output signal back into the time domain.
The optimal filter Wopt having an input corresponding to the antenna output y, is attained by minimizing the means square error between the desired signals and the estimated signal s. This optimal filter is described in terms of the desired s and means noise n at the antenna output [Simmer 92a]
                                          W            opt                    ⁡                      (            f            )                          =                                            Φ              ss                        ⁡                          (              f              )                                                                          Φ                ss                            ⁡                              (                f                )                                      +                                          Φ                                  nn                  _                                            ⁡                              (                f                )                                                                        (        3        )            where ΦSS(f) and Φnn(f) are the spectral power densities of the desired signal and the noise at the output of channel formation. This result follows from the following assumptions:                A1: The signal xi(n) incident on each sensor is modeled as the sum of the desired signal plus the noise according to eq. 1,        
A2: The noise ni(n) and the desired signal s(n) are uncorrelated,
A3: The noise spectral power densities are identical at each sensor, namely, Φnnf(f)=Φnn)f)
where i=1 . . . N,
A4: The noises of the sensors are uncorrelated (the interspectral power densities Φn,nf(f) are zero when i≠j),
A5: The input signal xi(n) are perfectly reset to be in phase with s(n).
A priori, the two values ΦSS(f) and Φnn(f) required to calculate Wopt(f) are unknown and it is difficult to estimate them. In the methods known in the state of the art, ΦSS(f) and Φnn(f) are estimated on the basis of signals incident on different sensors, Illustratively, assuming the noises detected by each microphone being uncorrelated, the estimate of the spectral power density (hereafter SPD) of the desired signal ΦSS(f) may be attained by estimating the interspectral power densities (hereafter IPD) Φvivj(f) of the microphone signals i and j that were reset to be in phase. In that event the spectral magnitudes Φvivj(f) and Φvivj(f) may be written asΦvivj(f)=ΦSS(f)+Φnn(f)  (4)Φvivj(f)=ΦSS(f), i≠j  (5)
One way of estimating Wopt(f) is to use an average of these spectral and interspectral power densities respectively in the denominator and in the numerator, from
                                          W            ^                    ⁡                      (            f            )                          =                                            2                              N                ⁡                                  (                                      N                    -                    1                                    )                                                      ⁢                          γ              ⁡                              (                                                      ∑                                          i                      =                      1                                                              N                      -                      1                                                        ⁢                                                                          ⁢                                                            ∑                                              j                        =                                                  i                          +                          1                                                                    N                                        ⁢                                                                                  ⁢                                                                                                                        Φ                            ^                                                                                                              v                              i                                                        ⁢                            v                                                                          j                                            ⁡                                              (                        f                        )                                                                                            )                                                                        1              N                        ⁢                                          ∑                                  i                  =                  1                                N                            ⁢                                                          ⁢                                                                    Φ                    ^                                                                              v                      i                                        ⁢                                          v                      i                                                                      ⁡                                  (                  f                  )                                                                                        (        6        )            where γ(.)=Ret(.) or γ(.)=[.],
The use of the module operator or the real part γ(.) is valid because of the magnitude which must be estimated in the numerator ΦSS(f) and which must be real and positive. The notation ^ means the (statistical) estimate of the particular value.
The estimator Ŵ(f)|γ(.)−Re(.) was proposed by R. Zelinski [Zelinski 88] to be used in the time domain. [Simmer 92a indicates estimating and filtering are carried out in the frequency domain. Ŵ(f)|γ(.)=|.| is an extension of two-sensor processing described by Allan [Allen 77] to an arbitrary number of sensors. Indeed eq. 6, when considered algorithmically, represents two estimating methods using the Wiener filter, namely Ŵ(f)|γ(.)=Re(.) and Ŵ(f)|γ(.)=|.|. 
Another estimator which uses the SPD of the antenna output signal, Φyy(f), was proposed by Simmer [Simmer 92b], namely
                                          W            ^                    ⁡                      (            f            )                          =                                            2                              N                ⁡                                  (                                      N                    -                    1                                    )                                                      ⁢                          γ              ⁡                              (                                                      ∑                                          i                      =                      1                                                              N                      -                      1                                                        ⁢                                                                          ⁢                                                            ∑                                              j                        =                                                  i                          +                          1                                                                    N                                        ⁢                                                                                  ⁢                                                                                            Φ                          ^                                                                                                      v                            i                                                    ⁢                                                      v                            i                                                                                              ⁡                                              (                        f                        )                                                                                            )                                                                        ∑                              i                =                1                            N                        ⁢                                                  ⁢                                                            Φ                  ^                                yy                            ⁡                              (                f                )                                                                        (        7        )            
The spectral magnitudes required to estimate the filter W(f), in this instance Φyy(f), Φvivj(f), and {circumflex over (Φ)}yy(f) must be estimated in turn from the signals Vi(f) and Y(f). In practice, using voice signals and a post-filter in an actual environment requires estimating to reliably monitor the non-stationary nature of such signals, while nevertheless guaranteeing admissible quality of estimation. In the Figure, the block 220 corresponds to the processing phase wherein {circumflex over (Φ)}vivi(f), {circumflex over (Φ)}vivj(f) and Φvivj(f) are estimated.
The receiving systems for multi-sensor antennas such as described above—whether or not using post-filtering—do not provide noise elimination in the absence of a desired signal. Moreover, when post-filtering is used, the noise (i.e. the interfering signal(s)), if attenuated by the post-filter, is also distorted by it. In many applications and in particular with respect to sound pickup, distorting an interfering signal, such as that generated by an interfering source within a sound receiving zone, produces an especially bothersome effect.
The basic problem to be solved by the present invention is to ascertain whether a desired signal is in fact present in a receiving zone of a multi-sensor antenna.
A first subsidiary problem to the basic goal of the embodiment of the present invention is to determine the incidence, i.e., the direction of an incoming signal, of any desired signal in the receiving zone.
A second subsidiary problem basic to another embodiment of the invention is to suppress the interference effect when the desired signal is deemed to be absent.